Yuancong Chai, Yuefei Zhu^*, Wei Lin, Ding Li

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1223-1243, 2024, DOI:10.32604/cmc.2024.049904

Abstract With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has become a core key technology in network supervision. In recent years, many different solutions have emerged in this field. Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-level features of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites, temporal features can exhibit significant variations due to changes in communication links and transmission quality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission. Faced with these challenges, identifying… More >

Yan Dong^1,2, Kang Zhao¹, Liang Gao¹, Hao Li^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1-18, 2024, DOI:10.32604/cmc.2024.048870

Abstract With the continuous advancement in topology optimization and additive manufacturing (AM) technology, the capability to fabricate functionally graded materials and intricate cellular structures with spatially varying microstructures has grown significantly. However, a critical challenge is encountered in the design of these structures–the absence of robust interface connections between adjacent microstructures, potentially resulting in diminished efficiency or macroscopic failure. A Hybrid Level Set Method (HLSM) is proposed, specifically designed to enhance connectivity among non-uniform microstructures, contributing to the design of functionally graded cellular structures. The HLSM introduces a pioneering algorithm for effectively blending heterogeneous microstructure interfaces. Initially, an interpolation algorithm is… More >

Yuhang Wang^1,*, Zhiqin He¹, Qinmu Wu¹, Tingsheng Lu², Yu Tang¹, Maoyun Zhu¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1377-1398, 2024, DOI:10.32604/cmc.2024.047379

Abstract This paper addresses the common orthopedic trauma of spinal vertebral fractures and aims to enhance doctors’ diagnostic efficiency. Therefore, a deep-learning-based automated diagnostic system with multi-label segmentation is proposed to recognize the condition of vertebral fractures. The whole spine Computed Tomography (CT) image is segmented into the fracture, normal, and background using U-Net, and the fracture degree of each vertebra is evaluated (Genant semi-qualitative evaluation). The main work of this paper includes: First, based on the spatial configuration network (SCN) structure, U-Net is used instead of the SCN feature extraction network. The attention mechanism and the residual connection between the… More >

MARKO SKELIN^1,2,3,*, BRUNA PERKOV-STIPIČIN¹, SANJA VUŠKOVIĆ⁴, MARINA ŠANDRK PLEHAČEK⁵, ANE BAŠIĆ⁶, DAVID ŠARČEVIĆ⁷, MAJA ILIĆ⁸, IVAN KREČAK^2,3,9

Oncology Research, Vol.32, No.5, pp. 807-815, 2024, DOI:10.32604/or.2024.048948

Abstract Background: The European Society for Medical Oncology (ESMO) guidelines are among the most comprehensive and widely used clinical practice guidelines (CPGs) globally. However, the level of scientific evidence supporting ESMO CPG recommendations has not been systematically investigated. This study assessed ESMO CPG levels of evidence (LOE) and grades of recommendations (GOR), as well as their trends over time across various cancer settings. Methods: We manually extracted every recommendation with the Infectious Diseases Society of America (IDSA) classification from each CPG. We examined the distribution of LOE and GOR in all available ESMO CPG guidelines across different topics and cancer types.… More >

Wei Wu^*, Yuan Zhang, Yunpeng Li, Chuanyang Li, Yan Hao

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 537-555, 2024, DOI:10.32604/cmes.2024.049174

Abstract Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities. Additionally, it leverages inter-modal correlation to enhance recognition performance. Concurrently, the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features. Nevertheless, two issues persist in multi-modal feature fusion recognition: Firstly, the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities. Secondly, during modal fusion, improper weight selection diminishes the salience of crucial modal features, thereby diminishing the overall recognition performance. To address these two issues, we introduce an enhanced DenseNet multimodal recognition network… More > Graphic Abstract

Hasan J. Alyamani^*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1129-1142, 2024, DOI:10.32604/cmes.2024.047756

Abstract The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis, a condition with significant clinical implications. However, endoscopic assessment is susceptible to inherent variations, both within and between observers, compromising the reliability of individual evaluations. This study addresses this challenge by harnessing deep learning to develop a robust model capable of discerning discrete levels of endoscopic disease severity. To initiate this endeavor, a multi-faceted approach is embarked upon. The dataset is meticulously preprocessed, enhancing the quality and discriminative features of the images through contrast limited adaptive histogram equalization (CLAHE). A diverse array of data augmentation… More > Graphic Abstract

Zhuo Huang, Ye Tian, Yifan Zhang, Tielin Shi, Qi Xia^*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 711-733, 2024, DOI:10.32604/cmes.2024.045411

Abstract Stiffened structures have great potential for improving mechanical performance, and the study of their stability is of great interest. In this paper, the optimization of the critical buckling load factor for curved grid stiffeners is solved by using the level set based density method, where the shape and cross section (including thickness and width) of the stiffeners can be optimized simultaneously. The grid stiffeners are a combination of many single stiffeners which are projected by the corresponding level set functions. The thickness and width of each stiffener are designed to be independent variables in the projection applied to each level… More >

Zhuoqun Xia¹, Hangyu Hu¹, Wenjing Li^2,3, Qisheng Jiang¹, Lan Pu¹, Yicong Shu¹, Arun Kumar Sangaiah^4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 409-430, 2024, DOI:10.32604/cmes.2024.030052

Abstract Early screening of diabetes retinopathy (DR) plays an important role in preventing irreversible blindness. Existing research has failed to fully explore effective DR lesion information in fundus maps. Besides, traditional attention schemes have not considered the impact of lesion type differences on grading, resulting in unreasonable extraction of important lesion features. Therefore, this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator (MPAG) and a lesion localization module (LLM). Firstly, MPAG is used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained… More >

ÇAĞLA GÜL GÜLDİKEN, LEVENT AKYALÇIN, HASAN FERDİ GERÇEL^*

Journal of Polymer Materials, Vol.36, No.4, pp. 337-349, 2019, DOI:10.32381/JPM.2019.36.04.4

Abstract In the present study, polybenzimidazole (PBI) polymer which has a great importance in polymer electrolyte membrane (PEM) research was synthesized via the solution polycondensation method, under various synthesis conditions to put forward the effect of the synthesis parameters on the properties of the synthesized polymer and consequently to the quality of the prepared polymer electrolyte membrane. The synthesized polymers were characterized in terms of their chemical structure, thermal degradation behaviour, and molecular weight by Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance spectroscopy (¹ H NMR), thermogravimetric analysis (TGA). The Mechanical properties, acid-doping level capacity, proton conductivity, and fuel cell… More >

Mengqi Zhang¹, Jiazhao Chai², Jianxiang Cao³, Jialing Ji³, Tong Yi^4,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3743-3762, 2024, DOI:10.32604/cmc.2024.048486

Abstract In recent years, deep learning methods have developed rapidly and found application in many fields, including natural language processing. In the field of aspect-level sentiment analysis, deep learning methods can also greatly improve the performance of models. However, previous studies did not take into account the relationship between user feature extraction and contextual terms. To address this issue, we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method. To be specific, we design user comment feature extraction (UCFE) to distill salient features from users’ historical comments and transform them into representative user feature vectors.… More >